263 related articles for article (PubMed ID: 33231527)
1. Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.
Magudia K; Bridge CP; Bay CP; Babic A; Fintelmann FJ; Troschel FM; Miskin N; Wrobel WC; Brais LK; Andriole KP; Wolpin BM; Rosenthal MH
Radiology; 2021 Feb; 298(2):319-329. PubMed ID: 33231527
[TBL] [Abstract][Full Text] [Related]
2. Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.
Magudia K; Bridge CP; Bay CP; Farah S; Babic A; Fintelmann FJ; Brais LK; Andriole KP; Wolpin BM; Rosenthal MH
AJR Am J Roentgenol; 2023 Feb; 220(2):236-244. PubMed ID: 36043607
[No Abstract] [Full Text] [Related]
3. Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning.
Weston AD; Korfiatis P; Kline TL; Philbrick KA; Kostandy P; Sakinis T; Sugimoto M; Takahashi N; Erickson BJ
Radiology; 2019 Mar; 290(3):669-679. PubMed ID: 30526356
[TBL] [Abstract][Full Text] [Related]
4. Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.
Somasundaram E; Castiglione JA; Brady SL; Trout AT
AJR Am J Roentgenol; 2022 Aug; 219(2):326-336. PubMed ID: 35234481
[No Abstract] [Full Text] [Related]
5. Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment.
Graffy PM; Liu J; Pickhardt PJ; Burns JE; Yao J; Summers RM
Br J Radiol; 2019 Aug; 92(1100):20190327. PubMed ID: 31199670
[TBL] [Abstract][Full Text] [Related]
6. Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation.
Zopfs D; Bousabarah K; Lennartz S; Santos DPD; Schlaak M; Theurich S; Reimer RP; Maintz D; Haneder S; Große Hokamp N
Eur J Radiol; 2020 Sep; 130():109153. PubMed ID: 32717577
[TBL] [Abstract][Full Text] [Related]
7. Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes.
Lee MH; Zea R; Garrett JW; Graffy PM; Summers RM; Pickhardt PJ
Radiology; 2023 Feb; 306(2):e220574. PubMed ID: 36165792
[TBL] [Abstract][Full Text] [Related]
8. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.
Ye Z; Saraf A; Ravipati Y; Hoebers F; Zha Y; Zapaishchykova A; Likitlersuang J; Tishler RB; Schoenfeld JD; Margalit DN; Haddad RI; Mak RH; Naser M; Wahid KA; Sahlsten J; Jaskari J; Kaski K; Mäkitie AA; Fuller CD; Aerts HJWL; Kann BH
medRxiv; 2023 Mar; ():. PubMed ID: 36945519
[TBL] [Abstract][Full Text] [Related]
9. Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects.
Tram NK; Chou TH; Janse SA; Bobbey AJ; Audino AN; Onofrey JA; Stacy MR
Eur Radiol; 2023 Sep; 33(9):6599-6607. PubMed ID: 36988714
[TBL] [Abstract][Full Text] [Related]
10. Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning.
Paris MT
Lifestyle Genom; 2020; 13(1):28-31. PubMed ID: 31822001
[TBL] [Abstract][Full Text] [Related]
11. Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment.
Graffy PM; Sandfort V; Summers RM; Pickhardt PJ
Radiology; 2019 Nov; 293(2):334-342. PubMed ID: 31526254
[TBL] [Abstract][Full Text] [Related]
12. Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.
Park HJ; Shin Y; Park J; Kim H; Lee IS; Seo DW; Huh J; Lee TY; Park T; Lee J; Kim KW
Korean J Radiol; 2020 Jan; 21(1):88-100. PubMed ID: 31920032
[TBL] [Abstract][Full Text] [Related]
13. Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.
Tallam H; Elton DC; Lee S; Wakim P; Pickhardt PJ; Summers RM
Radiology; 2022 Jul; 304(1):85-95. PubMed ID: 35380492
[TBL] [Abstract][Full Text] [Related]
14. Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.
Lee SB; Cho YJ; Yoon SH; Lee YY; Kim SH; Lee S; Choi YH; Cheon JE
Eur Radiol; 2022 Dec; 32(12):8463-8472. PubMed ID: 35524785
[TBL] [Abstract][Full Text] [Related]
15. Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.
Ye Z; Saraf A; Ravipati Y; Hoebers F; Catalano PJ; Zha Y; Zapaishchykova A; Likitlersuang J; Guthier C; Tishler RB; Schoenfeld JD; Margalit DN; Haddad RI; Mak RH; Naser M; Wahid KA; Sahlsten J; Jaskari J; Kaski K; Mäkitie AA; Fuller CD; Aerts HJWL; Kann BH
JAMA Netw Open; 2023 Aug; 6(8):e2328280. PubMed ID: 37561460
[TBL] [Abstract][Full Text] [Related]
16. End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT.
Nowak S; Theis M; Wichtmann BD; Faron A; Froelich MF; Tollens F; Geißler HL; Block W; Luetkens JA; Attenberger UI; Sprinkart AM
Eur Radiol; 2022 May; 32(5):3142-3151. PubMed ID: 34595539
[TBL] [Abstract][Full Text] [Related]
17. Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment.
Islam S; Kanavati F; Arain Z; Da Costa OF; Crum W; Aboagye EO; Rockall AG
Clin Radiol; 2022 May; 77(5):e363-e371. PubMed ID: 35260232
[TBL] [Abstract][Full Text] [Related]
18. Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.
Lee YS; Hong N; Witanto JN; Choi YR; Park J; Decazes P; Eude F; Kim CO; Chang Kim H; Goo JM; Rhee Y; Yoon SH
Clin Nutr; 2021 Aug; 40(8):5038-5046. PubMed ID: 34365038
[TBL] [Abstract][Full Text] [Related]
19. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study.
Pickhardt PJ; Graffy PM; Zea R; Lee SJ; Liu J; Sandfort V; Summers RM
Lancet Digit Health; 2020 Apr; 2(4):e192-e200. PubMed ID: 32864598
[TBL] [Abstract][Full Text] [Related]
20. Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.
Perez AA; Noe-Kim V; Lubner MG; Graffy PM; Garrett JW; Elton DC; Summers RM; Pickhardt PJ
Radiology; 2022 Feb; 302(2):336-342. PubMed ID: 34698566
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
